Efficient Automated Mining of Fuzzy Association Rules

Abstract

Mining association rules is one of the important research problems in data mining. So, many algorithms have been proposed to find association rules in databases with either binary or quantitative attributes. One of these approaches is fuzzy association rules mining. However, most of the earlier algorithms proposed for mining fuzzy association rules assume that fuzzy sets are given. In this paper, we propose an automated method for autonomous mining of both fuzzy sets and fuzzy association rules. For this purpose, we first find fuzzy sets by using an efficient clustering algorithm, namely CURE, and then determine their membership functions. Finally, we decide on interesting fuzzy association rules. Experimental results show the efficiency of the presented approach for synthetic transactions.